How M&A Advisory Firms Build Target Lists

A target list is one of the most tangible deliverables an M&A advisor produces. When a client hires you to run a buy-side search, the list is the first thing they evaluate. A list full of obvious names — the companies the client already knows about — doesn't build confidence. A list that surfaces companies the client hasn't heard of but immediately recognizes as relevant does. The difference between those two outcomes increasingly comes down to how the list was built.

What does the traditional target list process actually look like?

Most advisory firms follow a version of the same workflow. An analyst opens PitchBook, runs a set of filters — industry codes, geography, revenue range, employee count — and exports a list. They cross-reference with LinkedIn to check whether companies are real and active. They Google individual names to fill in gaps. They pull in a few companies from the partner's personal network or from past engagements.

The whole process takes days. Sometimes a week. And the output, while functional, tends to look a lot like what the client could have built themselves with the same database access. The obvious names are there. The non-obvious ones are not, because keyword filters and industry codes don't surface companies that describe themselves differently than the search terms you used.

The problem isn't effort. The analysts are doing real work. The problem is that the tools constrain the output. When you search by SIC code and keyword, you only find companies that match those specific labels. The market is much larger than what fits neatly into a dropdown.

Why do the best target lists include companies the client hasn't heard of?

When a client reviews a target list, they're evaluating two things at once: completeness and insight. Completeness means the obvious names are there — the client expects to see them as a sanity check. Insight means the list also contains companies the client didn't know about that clearly fit the criteria.

That second part is what earns trust and wins follow-on mandates. An advisor who surfaces a company the client hadn't considered, one that turns out to be a strong strategic fit, has demonstrated something that the client's internal team couldn't do on their own.

The challenge is that these non-obvious targets don't announce themselves. They might be in the same market but use different language to describe what they do. They might be in an adjacent vertical that overlaps in ways that keyword filters can't express. They might be smaller or newer and not yet well-covered in the major databases. Finding them requires a different approach than running the same PitchBook search everyone else runs.

How does semantic search find targets that keyword search misses?

Keyword search is literal. If you search for "industrial automation software," you get companies that use those exact words. Semantic search works on meaning. It understands that "factory floor robotics controls," "manufacturing process optimization," and "industrial IoT for production lines" are all describing related businesses, even though the words don't overlap.

This matters enormously for target list construction. A client looking for acquisition targets in industrial automation doesn't care whether the company uses the word "automation" on its website. They care whether the company does something relevant. Semantic search closes that gap.

Radar uses this approach across its entire search layer. You describe the type of company you're looking for in natural language — as specifically or broadly as you want — and the search returns companies ranked by how closely they match the meaning of your description. The results regularly include companies that would never appear in a traditional filter-based search because they use different terminology, sit in adjacent categories, or aren't classified in the databases the way you'd expect.

What role does similar company search play in building target lists?

One of the most effective starting points for an M&A target list is a company the client already knows. It might be the client's own company, a known competitor, or a recent transaction in the space. Starting from a known anchor and searching for lookalikes produces a target list that's grounded in reality rather than in abstract filter criteria.

Radar's similar company search does this directly. Point it at a company and it finds others that are similar in what they do, who they serve, and how they operate. The results capture the kind of nuance that industry codes miss: two companies in different NAICS categories that are actually direct competitors, or a company in a completely different vertical that serves the same buyer persona with a complementary product.

For advisors, this is a natural workflow. The client says, "Find me companies like X." Instead of trying to reverse-engineer a set of filters that approximate what X does, you use X itself as the search query and get back a list of genuine lookalikes.

How do enrichment columns make the list more useful to the client?

A list of company names isn't a deliverable. What makes a target list valuable is the structured information alongside each name: headquarters, employee count, funding stage, revenue estimate, key products, customer type, recent news. Building this out manually — researching each company individually and filling in columns in a spreadsheet — is where most of the analyst time goes.

Agentic enrichment changes this. Radar lets you add custom enrichment columns that answer specific questions about every company on the list at once. "What is their primary product?" "Do they serve enterprise or mid-market?" "Have they raised institutional capital?" "What is their geographic footprint?" These questions get answered across the entire list in minutes, with source citations, rather than requiring an analyst to research each company one at a time.

This is especially powerful for client-specific criteria. Every mandate has its own nuances — maybe the client cares about whether targets have a recurring revenue model, or whether they serve a particular end market, or whether they have operations in a specific region. Custom enrichment columns let you tailor the list to those criteria without rebuilding the research from scratch each time.

How much faster is this than the traditional process?

The honest answer is that a target list that used to take a week can be built in hours. The semantic search surfaces a broader and more complete set of candidates in minutes. The similar company search generates lookalikes instantly. The enrichment columns fill in the structured data that used to require individual research on each target.

The speed matters, but it's not the main point. The main point is that the list is better. It includes companies that the traditional process would have missed. It's enriched with the specific data points the client cares about. And because the search is based on meaning rather than keywords, it captures the market more completely than any filter-based approach can.

For advisory firms running multiple mandates simultaneously, this also means the team can produce more lists without proportionally increasing headcount. The leverage is real.

What does this mean for how advisors compete?

M&A advisory is a relationship business, and that isn't changing. But the firms that can produce a materially better target list, faster, have an edge in winning mandates and keeping clients engaged through the process. When the first deliverable impresses the client — when the list includes names they hadn't considered that clearly fit — it sets the tone for the entire engagement.

The firms that are still building lists the old way will keep producing lists that look like everyone else's. The firms that adopt agentic tools will consistently surface targets that others miss. Over time, that difference in list quality becomes a difference in reputation.

Radar is built for this workflow. Describe what you're looking for in plain English, search by similar company, and enrich the full list with client-specific data points — all in one place. Try it free or book a demo to see how it works with a live mandate.